Since the seminal work of [1]}, 'Neural Style Transfer' algorithms have split into three major regimes, each sacrificing one of three properties: speed, visual quality, or generalization. Optimization-based algorithms (the same regime originally explored in [1]}) offer quality and generalization, but are quite slow. Style-specific neural methods offer speed and quality, but overfit to a particular artist/style and do not generalize well. 'Universal' neural methods train on a large dataset of styles and offer generalization and speed, but sacrifice visual quality.
In Chapter  I give an overview of the research in each of these regimes as well as non-neural style transfer algorithms and earlier research in texture synthesis.
